| Experiment code | 21.7.3.41 |
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| Experiment Title | Identification of Superior Sorghum Genotypes based on MTSI and MGIDI indices |
| Research Type | Departmental Research |
| Experiment Background | Introduction: Sorghum (jowar) is a hardy, drought-tolerant cereal crop widely grown in semi-arid regions of India, especially in Maharashtra, Karnataka, and Gujarat. It serves as a staple food, fodder, and industrial raw material due to its adaptability and nutritional value. Rich in protein, fibre, and micronutrients, sorghum plays a vital role in food security and sustainable agriculture.In the agricultural year 2023–24, Gujarat cultivated sorghum (jowar) over an area of approximately 40,000 hectares, yielding an estimated 50,000 tonnes of production. This results in a productivity of about 1,378 kg per hectare. While Gujarat's contribution to India's total sorghum area and production is modest, the state has maintained stable production levels over the years. Notably, the Tapi district consistently records the highest production figures, indicating favorable agro-climatic conditions for sorghum cultivation in that region.( Plant Archives) Nationally, India cultivated sorghum over approximately 4.08 million hectares in 2023–24, producing around 4.74 million tonnes. This translates to an average yield of 1.16 tonnes per hectare. Major sorghum-producing states include Maharashtra (37% of total production), Karnataka (22%), and Tamil Nadu (10%).( IPAD) Regarding exports, India shipped approximately 1,792 consignments of sorghum between November 2023 and October 2024. These exports were facilitated by 334 Indian exporters to 638 international buyers. However, this marked a 16% decline compared to the previous twelve-month period(Sorghum Exports from India – Volza) Nutritionally, sorghum is a gluten-free grain rich in protein, fiber, and essential micronutrients. Sorghum breeding focuses on developing high-yielding, drought and pest-resistant varieties suitable for diverse agro-climatic zones. Significant progress has been made in hybrid development for grain, fodder, and sweet sorghum types. However, challenges like shoot fly infestation, low adoption of improved varieties, and climate variability persist. Continued genetic improvement and farmer-oriented varietal dissemination are essential for sustainable . The Additive Main Effects and Multiplicative Interaction (AMMI) statistical model incorporates both additive and multiplicative components of the two-way data structure which can account more effectively for the underlying interaction patterns. In the multi-locational trials, the interaction between genotype and environment is very common. Traditional models like Eberhart–Russell (ER), AMMI, and GGE biplot have been widely used for stability and G×E interaction analysis in plant breeding. However, these models often focus on single traits, assume linear responses, and may not effectively handle multi-trait selection or complex interactions. They can also suffer from collinearity and provide limited insights into ideotype-based selection. The MTSI and MGIDI models are better than the AMMI model in plant breeding because they consider multiple traits and gene interactions simultaneously, providing a more holistic approach to selecting superior varieties. The MTSI focuses on optimizing the balance of traits, while the MGIDI accounts for complex gene interactions and direct influences on traits. In contrast, the AMMI model mainly addresses genotype-environment interactions, limiting its ability to handle multi-trait and genetic complexity. MGIDI (MULTI-TRAITIDEOTYPE DISTANCE INDEX )Model This model explain how it accounts for multiple gene interactions and their direct influence on plant traits. It is better than existing stability model like AMMI as taking in to account of all limitation .Emphasize the importance of understanding gene interactions in complex traits (e.g., yield, disease resistance, abiotic stress tolerance) and the limitations of traditional single- gene selection models. This also Explain how GWAS Genome-Wide Association Studies (GWAS) can be used in the MGIDI model to identify genes with direct influence and nteractions.With this motivation following objective has been framed |
| Experiment Group | Social Science |
| Unit Type | (02)EDUCATION UNIT |
| Unit | (12)NAVINCHANDRA MAFATLAL COLLEGE OF AGRICULTURE (NAVSARI) |
| Department | (247)Statistics Department, NMCA, Navsari |
| BudgetHead | (303/12712/02)303/03/REG/01782 |
| Objective |
Objectives: To estimate G×E interaction through different stability approaches Null hypothesis: |
| PI Name | (NAU-EMP-2015-000063)ALOK SHRIVASTAVA |
| PI Email | igkvalok@nau.in |
| PI Mobile | 9424242849 |
| Year of Approval | 2025 |
| Commencement Year | 2025 |
| Completion Year | 2026 |
| Research Methodology |
Statistical Methods The mean value of each experimental unit for various traits will be computed. These mean values computed for the various characters will be used for statistical analysis. Experimental details: RBD with 3 replication Analysis of variance Analysis of variance technique described by Panse and Sukhatme (1978) will be followed to test the differences among the genotypes for all the characters. Variability parameters The variability parameters viz., genotypic coefficient of variance and phenotypic coefficient of variance will be estimated as per the procedure suggested by Burton (1952) while, heritability and genetic advance will be calculated as per procedure suggested by Allard (1960). G x E interaction Stability analysis will be done using MTSI ( Muti-trait stability Index) and MGIDI (MULTI-TRAITIDEOTYPE DISTANCE INDEX ) Multi-Trait Selection Index (MTSI) : It enables simultaneous selection of genotypes based on multiple traits.Incorporates eritability and genetic correlations among traits.Better aligned with breeding goals which rarely focus on one trait alone. Multi-Trait Genotype–Ideotype Distance Index (MGIDI) Ranks genotypes based on their distance from an ideal genotype (ideotype).Uses factor analysis to group correlated traits and eliminate redundancy. Offers a clear, interpretable metric to select genotypes closest to the breeding target. Captures both performance and stability in one framework. OBSERVATIONS TO BE RECORDED: Characters:7
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(NAU-EMP-2015-000063) ALOK SHRIVASTAVA |
igkvalok@nau.in | 9424242849 | 22-12-2025 |
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